I want to become a Data Scientist.
I have completed the ML Specialization and have been working on few datasets sourced from Kaggle/UCI and other repositories to practice my skills.
I also wish to take the TF Professional certificate exam in future.
For further learning kindly suggest which would be a better path:
The first one because you need to learn about Neural Networks first!
So the NN basics covered in ML Specialization is not enough?
Also, does the DL Specialization deal with the latest developments such as Transformers?
Thank you for guiding.
Not enough and yes introduces transformers!
Thank you. This helps me decide a learning path confidently.
My humble opinion only. ML specialization - > DL Specialization (At the same time, do some Kaggle projects with existing datasets) → Gen AI with AWS specialization (If you are into Gen AI) and more hands on on Kaggle etc.
In the meantime, learn TF or PyTorch. After all these, you can decide on some other specialization like NLP, GAN etc depending on your interest. 
Definitely 1, or both concurrently.
The TF professional course references the DLS classes’ videos. It’s basically the labs that should be in the Deep Learning Specialization.
You will not understand what you are doing in the TF1 classes without the theory from DLS.
I found that the DLS introduced transformers, but it is frankly very brief and insufficient. Does DL.Ai offer a class that more substantially covers the topic?
Yes the Natural Language Specialization offers a better analysis I think.
My goal is to do some hands-on development in Gen AI (Text, Image, and Video). So far, I have completed ML Specialization and pursuing C5 of DL specialization.
What is the best learning path now to achieve my goal?
I know this is an old thread. I hope your experience will be my lighthouse.
Thanks in advance. Feel free to suggest modifications to my goal.
By “hands on development”, do you mean creating new Gen AI tools, or do you mean learning to use using existing tools?